In this paper, we propose visualizing results of a corpus-based study on text complexity using radar charts. We argue that the added value of this type of visualisation is the polygonal shape that provides an intuitive grasp of text complexity similarities across the registers of a corpus. The results that we visualize come from a study where we explored whether it is possible to automatically single out different facets of text complexity across the registers of a Swedish corpus. To this end, we used factor analysis as applied in Biber’s Multi-Dimensional Analysis framework. The visualization of text complexity facets with radar charts indicates that there is correspondence between linguistic similarity and similarity of shape across registers.

We explore the effectiveness of four feature representations – bag-of-words, word embeddings, principal components and autoencoders – for the binary categorization of the easy-to-read variety vs standard language. Standard language refers to the ordinary language variety used by a population as a whole or by a community, while the “easy-to-read” variety is a simpler (or a simplified) version of the standard language. We test the efficiency of these feature representations on three corpora, which differ in size, class balance, unit of analysis, language and topic. We rely on supervised and unsupervised machine learning algorithms. Results show that bag-of-words is a robust and straightforward feature representation for this task and performs well in many experimental settings. Its performance is equivalent or equal to the performance achieved with principal components and autoencorders, whose preprocessing is however more time-consuming. Word embeddings are less accurate than the other feature representations for this classification task.

We present initial results from an international and multi-disciplinary research collaboration that aims at the construction of a reference corpus of web genres. The primary application scenario for which we plan to build this resource is the automatic identification of web genres. Web genres are rather difficult to capture and to describe in their entirety, but we plan for the finished reference corpus to contain multi-level tags of the respective genre or genres a web document or a website instantiates. As the construction of such a corpus is by no means a trivial task, we discuss several alternatives that are, for the time being, mostly based on existing collections. Furthermore, we discuss a shared set of genre categories and a multi-purpose tool as two additional prerequisites for a reference corpus of web genres.

In this paper, we present an inferential model for text type and genre identification of Web pages, where text types are inferred using a modified form of Bayes’ theorem, and genres are derived using a few simple if-then rules. As the genre system on the Web is a complex phenomenon, and Web pages are usually more unpredictable and individualized than paper documents, we propose this approach as an alternative to unsupervised and supervised techniques. The inferential model allows a classification that can accommodate genres that are not entirely standardized, and is more capable of reading a Web page, which is mixed, rarely corresponding to an ideal type and often showing a mixture of genres or no genre at all. A proper evaluation of such a model remains an open issue.

The Web has triggered many adjustments in many fields. It also has had a strong impact on the genre repertoire. Novel genres have already emerged, e.g. blog and FAQs. Presumably, other new genres are still in formation, because the Web is still fluid and in constant change. In this paper we present an experiment that explores the possibility of automatically detecting the emerging textual patterns that are slowly taking shape on the Web. Emerging textual patterns can develop into novel Web genres or novel text types in the near future. The experimental set up includes a collection of unclassified web pages, two sets of features and the use of cluster analysis. Results are encouraging and deserve further investigation.